Toward model-free and reference-free quantitative ultrasound

Current spectral-based quantitative ultrasound (QUS) techniques rely on a reference or a calibration signal to obtain system-independent ultrasonic scattering parameters. Adoption of QUS clinically has been hindered by (1) the need to limit clinical ultrasonic scanner settings to account for a finite number of calibration signals or (2) the need to acquire calibration signals immediately before or after a setting is changed on an ultrasonic scanner, which can interrupt the workflow in the busy clinical environment. An additional factor that hinders the effectiveness of QUS clinically is the presence of layers over the tissue regions to be interrogated. Layer effects are not accounted for when using a reference phantom, reducing the technique's reliability. The dissertation presents experimental and computational approaches to improve the accuracy of QUS estimates in the presence of intervening layers using an in situ calibration target and the feasibility of reference-free quantitative ultrasound using a convolutional neural network. The sensitivity of CNN in a reference-free environment is assessed and experimentally validated. A model-free approach through the use of principal component analysis is proposed as a general method for tissue characterization using QUS and compared to a model-based approach. To address the effects of intervening layers on QUS accuracy and precision, an in situ calibration approach is proposed and experimentally verified to improve the QUS estimates.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2019

Author dc:creator

• Nguyen, Trong Ngoc

Contributors dc:contributor

• Oelze, Michael L.
• Do, Minh N.
• Boppart, Stephen A.
• Liang, Zhi-Pei

• Quantitative ultrasound, reference-free, fatty liver, in situ calibration

Statement dc:rights

• Copyright 2019 Trong Nguyen

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/105027

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/105027